Document clustering method and system

ABSTRACT

Document clustering method and system utilizing both the log-based clustering method and the content-based clustering method are disclosed. The method includes the steps of generating log-based document clusters and combining vectors from the log-based document clusters with individual document clusters for content-based clustering analysis. The log-based document clusters are generated by accessing the retrieval session log, clustering the retrieval sessions, and combining the documents opened during each of the sessions of session clusters.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.09/532,539 filed on Mar. 22, 2000, now U.S. Pat. No. 6,728,932 which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to document clusteringtechniques. More specifically, the present invention relates to documentclustering techniques incorporating both content-based and log-basedmethods to produce clusters that incorporate users' perspective.

BACKGROUND OF THE INVENTION

Information retrieval systems are concerned with locating documentsrelevant to a users information need from a collection of documents. Theuser describes his information need using a query consisting of a numberof words. The information retrieval systems compare the query with thedocuments in the collection and return the documents that are likely tosatisfy the information need.

Document clustering is often used to increase the efficiency andeffectiveness of the information retrieval systems. Clustering involvesthe grouping of similar or otherwise related documents. In the contextof information retrieval, document clustering identifies groups ofsimilar documents, usually on the basis of terms that the documents havein common. Closely associated documents tend to be relevant to samequeries or requests. Therefore, clustering of documents increasesefficiency of the information retrieval systems. Further, clustering ofdocuments also aids in browsing of the document collection. Relateddocuments can be co-located to enhance browsing.

Cluster analysis methods are usually based on measurements of similaritybetween objects, these objects being either individual documents orclusters of documents. Traditionally, interdocument similarity wasdetermined by analyzing the contents of the documents. The content-basedclustering method assumes that documents are represented by lists ofmanually or automatically assigned terms, keywords, phrases, indices, orthesaural terms that describe the content of the documents.

Because the content-based clustering approach analyzes each and everydocument to be clustered, the result is complete and stable. Using thecontent-based clustering approach, the entire collection of thedocuments can be clustered, and the clusters do not change as long asthe document collection and the keywords do not change.

The content-based clustering method is widely used on the Internet as amethod of organizing information. Ever-increasing amount of informationis becoming available via the Internet and the World Wide Web (the“Web”). However, because of the decentralized nature of the informationpresented, it is becoming increasingly difficult for a user to findrelevant information regarding a particular subject. To assist the userto locate relevant information on the Web, many portal sites maintaindirectories built upon content-based clustering of the web pages.

Portals are Internet sites that organize, or categorize web pages intovarious topics and offer topic-based or keyword-based organization ofthe web pages to the user. However, because the portals' topics and thekeywords are determined by the portal providers, the topics, thekeywords, or the assignment of the web pages to these topics or keywordsdo not reflect the perspectives and the interests of the users. In fact,the users may find the portals' organization or clustering of the webpages to be stifling and non-sensible.

Additionally, the organization of the web pages into the portals' topicsand categories cannot account for differences between differentdemographic groups of users. For example, people of different ages,gender, or occupations are likely to prefer different categorization andclustering of the web pages. Unfortunately, regardless of the users'preferences, the portals offer the same categorization of the web pagesas generated by the portal providers. Some portals offer facilities forthe user to “customize” the portal. However, these facilities typicallyprovide limited functions for the user to select, from thealready-determined topics and categories, which topics and categories todisplay when the user links to the portal. And, typically, thesecustomization facilities do not allow users to create customized topicsor categories, or to assign web pages to certain categories forcustomized clustering of the web pages.

Further, the content-based clustering method, because of its staticnature, cannot adapt to changing preferences of the users and theaddition of new topics, categories, or areas of interest.

To overcome some of the shortcomings of the content-based clusteringmethod, log-based clustering technique has been proposed. Recently, ithas been shown that documents can be clustered based on retrieval systemlogs maintained by an information retrieval system such as web serveraccess logs. Using web server access logs, it has been shown thatsimilar pages tend to be accessed together by users. Under the log-basedclustering method, the interdocument similarity can be based uponwhether the documents were accessed together during retrieval sessionsby the user.

Since the clustering of documents for each user is based on retrievalsystem logs, documents (e.g., Web pages) that users found to be similarfall into the same cluster, thereby reflecting the “similarity notion”of users. As user access patterns change, the clusters will also changegiving the clusters a “dynamic” nature. And, since the log-basedclustering method can be based on recent retrieval system logs for eachuser, the clustering can adopt to the changing tastes and perspective ofthe user.

However, the log-based clustering method produces document clusterswhich are inherently incomplete. This is because the log-basedclustering method clusters only those documents that are accessed bysome users. In an environment like the Internet where millions uponmillions web pages exist, only a tiny portion would be clustered underthe log-based clustering method. The remaining web pages are notclustered at all.

Accordingly, there remains a need for a document clustering method thatincorporates users' perspective while accounting for documents notaccessed by the user and that overcomes the disadvantages set forthpreviously.

SUMMARY OF THE INVENTION

According to one aspect of the present invention, a method forclustering documents is disclosed. The documents are represented in ahybrid matrix, and the hybrid matrix is clustered by a content-basedclustering algorithm. There is one vector per document in the hybridmatrix. For those documents that are accessed in the session logs, alog-based document clustering vector is constructed in the hybridmatrix. For all other document, a vector based on keywords isconstructed.

To form the log-based cluster document vector, a corresponding log-basedcluster document must be generated. The log-based cluster document isgenerated by accessing retrieval session logs and clustering them intosession clusters. Then, the log-based cluster document is generated foreach session cluster by concatenating the documents that were openedduring the sessions in that session cluster.

According to another aspect of the present invention, an apparatus forclustering is documents includes storage for storing retrieval sessionlogs and a processor, connected to the storage, for performing the stepsof the present invention. The apparatus may further include memory,connected to the processor, for storing intermediate results includingthe hybrid matrix. The storage and the memory is preferably machinereadable memory devices encoded with data structure for clusteringdocuments including the hybrid matrix, retrieval session logs, and theinstructions for the processor.

Other aspects and advantages of the present invention will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating a preferred method of clusteringdocuments according to the present invention.

FIG. 2 is a block diagram illustrating a data processing system in whichthe document clustering method and system according to the presentinvention can be implemented.

FIG. 3 is a block diagram illustrating in greater detail the documentclustering module of FIG. 2.

FIG. 4 is a block diagram illustrating in greater detail the hybridmatrix builder of FIG. 3.

DESCRIPTION OF A PREFERRED EMBODIMENT

As shown in the drawings for purposes of illustration, the presentinvention is embodied in a novel hybrid clustering method and system forclustering a collection of documents while accounting for the users'tastes and perspectives on the documents to be clustered.

The content-based clustering method clusters the entire collection ofdocuments based upon topics or keywords. However, the users do notparticipate in the selection of the topics and the keywords or theclustering process. Therefore, clustering may be of limited use tovarious groups of users having variety of perspectives or interests. Thelog-based clustering method clusters documents based upon retrievalsession logs of the users. Accordingly, the resulting document clustersmay be highly relevant and useful for users. However, the log-basedclustering method provides for clustering of only those documentsalready accessed by some users. Therefore, the clustering is inherentlyincomplete. The present invention combines the advantages of bothclustering techniques to produce a customized, relevant clustering ofdocuments encompassing the entire collection of documents.

The present invention will be described with reference to numerousdetails set forth below, and the accompanying drawings will illustratethe invention. The following description and the drawings areillustrative of the invention and are not to be construed as limitingthe invention. Numerous specific details are described to provide athorough understanding of the present invention. However, in certaininstances, well-known or conventional details are not described in orderto avoid obscuring the present invention in unnecessary detail. In thedrawings, the same element is labeled with the same reference numeral.

Generate Log-based Document Cluster

FIG. 1 is a flowchart illustrating a preferred method of clusteringdocuments according to the present invention. FIG. 2 is a diagramillustrating a data processing system 26 configured to cluster documentsaccording to the present invention. FIG. 3 is a block diagramillustrating in greater detail the document clustering module 42 of FIG.2. FIG. 4 is a block diagram illustrating in grater detail the hybridmatrix builder 80 of FIG. 3. The following discussion refers to FIGS.1–4.

To clearly explain the present invention, assume that there is acollection, D, of documents to be clustered. The collection D has Nnumber of documents with each document identifiable as di where i is anindexing number between 0 and N and where dn is the last document. Onthe Internet, the value of N may be very large and easily exceed manymillions. Also assume that the collection D is accessible to a user viathe information retrieval system which keeps a log of the user'sretrieval sessions.

Retrieval session logs 34 are typically kept on a storage device 28 of aweb server or another information retrieval system. The first step,indicated by block 12, is to access retrieval session logs (e.g.,session logs 34 of FIG. 3). The storage 28 may also contain thecollection D of the documents 36 to be clustered. Each retrieval sessionlog 34 contains the query used to retrieve documents, number ofdocuments found to satisfy the query, and a list of documents opened bythe user. The document retrieved and read by the user is referred to asan opened document. TABLE 1 below illustrates M sample retrievalsession, each of which is denoted sj where the value of j denotes thej^(th) session, qj denoting the query used for the j^(th) session, rjdenoting the number of retrieved documents at the j^(th) session, andthe list of documents opened during the session; the last session isdenoted sm:

TABLE 1 Query No. of docs. Session Used found Opened Document List s1(session 1) q1 R1 d1 d5 d6 s2 q2 R2 d2 d4 d17 d78 s3 q3 R3 d5 d6 * * *Sm qm Rm d4 d17

In addition to the opened document list, other factors may be used torank the relevance of documents in the logs. For example, the length oftime that a document was opened may indicate that the document is morerelevant to the corresponding query. Also, the last document opened forreview by the user may be ranked higher in the relevance because it maybe assumed that the last document opened contained the information theuser was seeking.

It has been shown that, in the case of web servers, web pages accessedin the same user session tend to be related. And, if two retrievalsessions are related, then the documents accessed in those retrievalsessions are also related. Accordingly, to generate log-based documentclusters, the retrieval sessions are first clustered into sessionclusters, as indicated by block 14.

To cluster retrieval sessions, the retrieval sessions are firstrepresented in a manner suitable for applying a clustering algorithm. Tocluster retrieval sessions, session vector matrix is generated. Forexample the session vector matrix is represented in FIG. 3 by “sessionsvectors 64.” In the session vector matrix, each session is representedas P-dimensional vector where P is a parameter value. Each retrievalsession is then converted to a Boolean vector in the P-dimensionalspace. That is, the Boolean vector corresponding to a retrieval sessionsj contains a 1 for the p^(th) dimension if the document correspondingto the p^(th) dimension is included in the list of opened documents forsession sj. The value of P can be any number. In the preferredembodiment, the value P is the number of unique documents opened for allof the retrieval sessions under consideration. In an extreme case, ifall of the documents in the collection of documents were opened duringat least one retrieval session, then the value of P is equal to thevalue of N (the number of documents in the collection of documents).However, this is an unlikely event in practice.

For example, if TABLE 1 were to reflect all the documents opened duringall the retrieval sessions, then TABLE 2 below represents all of thesession vector matrix. Here, the value of P is seven (7) because therewere seven (7) unique documents opened during all of the retrievalsessions.

TABLE 2 P^(th) dimension → 1 2 3 4 5 6 7 Document id → d1 d2 d4 d5 d6d17 d78 s1 (session 1) vector 1 0 0 1 1 0 0 s2 vector 0 1 1 0 0 1 1 s3vector 0 0 0 1 1 0 0 sm vector 0 0 1 0 0 1 0

Each data row of TABLE 2 represents a Boolean session vector for aretrieval session. The session vectors are Boolean vectors because eachelement of the session vectors is a Boolean value reflecting whether ornot the corresponding document was opened during that session. In theexample of TABLE 2, during session s1, documents d1, d5, and d6 wereopened. Therefore, the session vector for s1 includes a Boolean 1 forthe vector positions corresponding to the documents d1, d5, and d6, anda Boolean 0 for all other vector positions.

Then, the session vector matrix, represented here by TABLE 2, isclustered using a standard clustering algorithm to cluster similar orrelated sessions. In the example, for the purposes of furtherillustration, assume that sessions s1 and s3 are clustered togetherforming session cluster S(1,3) and sessions s2 and sm are clusteredtogether forming session cluster S(2,m). Note that during each of thesessions s1 and s3, the documents d5 and d6 were opened. And, duringeach of the sessions s2 and sm, the documents d4 and d17 were opened.Session clusters are referred to in FIG. 3 as “session clusters 38.”

The log-based document clusters are then formed for each session clusterby combining all of the documents opened during any of the sessions ofthe session cluster. This step is represented by block 16. For example,the log-based document cluster for session cluster S(1,3) is acombination of the documents d1, d5, and d6 because these threedocuments were opened at least once during sessions s1 or s3 which formthe session cluster S(1,3). Likewise, for session cluster S(2,m), thelog-based document cluster is a combination of documents d2, d4, d17,and d78. In the preferred embodiment, the combination of documents isformed by concatenating the documents. Accordingly, in the preferredembodiment, the log-based document cluster is a “super document”(referred to in FIG. 4 as “super documents 114”) that is a concatenationof its component documents.

For convenience, the log-based document cluster combining documents d1,d5, and d6 is referred to as G(1, 5, 6), and the log-based documentcluster combining documents d2, d4, d17, and d78 is referred to as G(2,4, 17, 78). And, the set of all documents, which have been combined to alog-based document cluster, will be referred to as set L. In theexample, set L comprises documents d1, d2, d4, d5, d6, d17, and d78.

Content-Based Clustering Using Log-Based Document Cluster Vectors

At this stag, the collection D of all documents can be categorized intoone of two broad categories. First, there are documents, which have beencombined into one or more log-based document clusters. These are thedocuments belonging to set L. Second, there are documents, which havenot been combined into any of the log-based document clusters becausethey were not opened during any of the retrieval sessions. Thesedocuments can be grouped into a set denoted D−L (collection D minus setL).

Then, the collection D of all documents can be clustered using thestandard content-based clustering technique using a hybrid matrixcomprising document vectors and log-based document cluster vectors asfollows. For each of the documents in set D−L, a standard documentvector is generated. Assume, for the purposes of illustration, that thecontent-based clustering is to be performed over a set of keywords, W,having T members where T is a natural number. Then, for each of thedocuments in the set D−L, a T-dimensional document vector is generated.This step illustrated by block 20.

However, for each of the documents in the set L, a T-dimensional vectorgenerated from the log-based document cluster to which the document wascombined. This step is illustrated by block 18. Since the log-baseddocument clusters are documents themselves, the vectors are generatedthe same way the vectors are generated for any document.

Continuing with the example, the documents d1, d5, and d6 were combinedto form the log-based document cluster G(1, 5, 6), and documents d2, d4,d17, and d78 were combined to form the log-based document cluster G(2,4, 17, 78). Then, documents d1, d2, d4, d5, d6, d17, and d78 are membersof the set L. All other documents are members of the set D−L. ClustersG(1, 5, 6) and G(2, 4, 17, 78) are “larger” documents formed from theirrespective components.

The individual document vectors for the documents of the set D−L and thelog-based document cluster vectors are combined to form a hybrid matrixof vectors. Step 22. For the documents belonging to set D−L, standarddocument vectors are generated. For each of the documents belonging toset L, the corresponding log-based document cluster vector is used inits place. TABLE 3 below illustrates the hybrid matrix formed inaccordance with the present example.

TABLE 3 KEYWORDS Documents w1 w2 w3 w4 * * * wq d1 (G(1, 5, 6) vector)d2 (G(2, 4, 17, 78) vector) d3 document vector d4 (G(2, 4, 17, 78)vector) d5 (G(1, 5, 6) vector) d6 (G(1, 5, 6) vector) d7 document vectord8 document vector * * * d16 document vector d17 (G(2, 4, 17, 78)vector) d18 document vector * * * d77 document vector d78 (G(2, 4, 17,78) vector) d79 document vector * * * Dn document vector

In TABLE 3, each row is a vector, and the entire table represents thehybrid matrix. The hybrid matrix (referred to as hybrid matrix 40 inFIGS. 3 and 4) is clustered using a content-based clustering method. Foreach of the documents (e.g., documents 36 in FIGS. 3 and 4)*belonging tothe set D−L (these are the documents that were not opened during anyretrieval session), a document vector is generated and used. This stepis illustrated by block 20. Since each of the document vectors of thedocuments in the set D−L represents an individual document, thesedocument vectors can be referred to as individual document vectors. InTABLE 3, document vectors for the following documents are illustrated asindividual document vectors: d7, d8, d16, d18, d77, d79, and dn.

For the documents belonging to set L (documents opened during aretrieval session), individual document vectors are not used. Instead, avector generated from the log-based document cluster to which thedocument has been combined is used. In the example, document d1 wascombined into the log-based document cluster G(1, 5, 6). Therefore, avector generated for G(1, 5, 6) is used in place of d1. In fact, thelog-based document cluster vector for G(1, 5, 6) is used for each of thedocuments d1, d5, and d6.

It is important that the session clusters be represented in such a wayso that when documents (both those accessed in sessions and those notaccessed in sessions) are clustered using a content based clusteringmethod, user preference is reflected in the resulting clusters. In thepreferred embodiment, all the documents of a session are represented insuch a way so that the Euclidean distance of all documents in the samesession is made to be the same when a content-based cluster is appliedto the hybrid matrix. By making the Euclidean distance the same, thepresent invention ensures that documents of the same session areclustered together in the same content-based cluster in order to reflectuser perspective. Alternatively, other methods can be used to representall documents in the same session so that the Euclidean distance betweenthese documents is the same or has a minimal differences so that thedocuments from the same session are clustered when a content basedclustering method is applied, thereby providing user perspective in theclustering.

It is noted that in the prior art, the output of a log-based clusteringmethod is inherently not suitable as an input to a content basedclustering method. In contrast, the present invention provides a novelmethod of representing the output of a log-based clustering method insuch a manner so that not only is the output of the log-based clusteringmethod suitable as an input to content based clustering, but therepresentation also provides user perspective to the content basedclustering method. In other words, the log-based cluster documentvectors provide both user perspective, by clustering all the documentsof a session together, and content so that a content based methodclusters other documents with similar content to these documents.

When the hybrid matrix is complete 22, in processing step 24, thecontent-based clustering technique is applied to cluster the documentsof the collection D.

To summarize, in accordance with one embodiment of the present inventionthe following steps are performed. In step 12, session logs arereceived. In step 14, the session logs are clustered into sessionclusters. In step 16, a log-based cluster document is generated for eachsession cluster. In step 17, a plurality of documents that includes atleast one document that has been accessed in one session is received. Instep 18, for each session cluster, a log-based cluster document vectoris generated based on the corresponding log-based cluster document, andeach document in that session cluster is replaced with the log-basedcluster document. In step 20, for each document not accessed in any ofthe sessions, an individual document vector based on the document isgenerated. In step 22, a hybrid matrix that has at least one individualdocument vector and at least one log-based cluster document vector isgenerated. In step 24, the hybrid matrix is clustered to generateclusters that incorporate user perspective.

FIG. 2 is a block diagram illustrating a data processing system 26 inwhich the document clustering method and system according to the presentinvention can be implemented. In the preferred embodiment, a system forclustering documents in accordance with the present invention isimplemented in a computing machine 26 having storage 28 for maintaininguser retrieval session logs 34. The storage 28 may also contain thedocuments 36 to be clustered. A processor 30, connected to the storage28, can be programmed to perform the steps illustrated by the flow chartof FIG. 1 and discussed in detail herein above. Specifically, processor30 can be programmed to perform the steps of accessing the retrievalsession logs 34, clustering the retrieval sessions into sessionclusters, generating the log-based document clusters, generating thehybrid matrix by generating vectors for the documents of the set D−L andfor the log-based document clusters, and clustering the documents basedon the hybrid matrix. In order to perform these tasks, the processor 30may be connected to media 32 for holding the session clusters 38 or thehybrid matrix 40. The media 32 may also include the document clusteringmodule 42 including instructions, which when executed, cause theprocessor 30 to perform the steps of the present invention.

The media 32, having the document clustering module 42, may beincorporated in office equipment (e.g., a computer) or separate fromoffice equipment. When incorporated in office equipment, the media 32,having the document clustering module 42 embodied therein, can be in theform of a volatile or non-volatile memory (e.g., random access memory(RAM), read only memory (ROM), etc.). When incorporated separate fromthe office equipment, the media 32, having the document clusteringmodule 42 embodied therein, can be in the form of a computer-readablemedium, such as a floppy disk, compact disc (CD), etc.

FIG. 3 is a block diagram illustrating in greater detail the documentclustering module 42 of FIG. 2. In accordance with one embodiment of thepresent invention, the document clustering module 42 includes a sessionvector generation module 60, a session cluster generation module 70, ahybrid matrix builder 80, and a topic generation module 90.

The session vector generation module 60 receives session logs 34 andbased thereon generates session vectors 64. The session clustergeneration module 70 is coupled to the session vector generation module60 for receiving the session vectors 64, and based thereon, generatessession clusters 38 (see steps 12 and 14 of FIG. 1).

The hybrid matrix builder 80 is coupled to the session clustergeneration module 70 for receiving the session clusters 38, receivesdocuments 36, and based thereon, generates a hybrid matrix 40. Forexample, the hybrid matrix builder 80 can perform steps 16 through 22 ofFIG. 1. The hybrid matrix builder 80 is described in greater detailhereinafter with reference to FIG. 4.

The topic generation module 90 is coupled to the hybrid matrix builder80 for receiving the hybrid matrix 40, and based thereon, generatestopics 94 (i.e., clusters incorporating users' perspective) (see step 24of FIG. 1).

FIG. 4 is a block diagram illustrating in greater detail the hybridmatrix builder 80 of FIG. 3. In accordance with one embodiment of thepresent invention, the hybrid matrix builder 80 includes a sessiondocument generation module 110 and a document modification module 120.The session document generation module 110 is coupled to the sessioncluster generation module 70 for receiving the session clusters 38, andbased thereon, generates super documents 114. The document modificationmodule 120 is coupled to the session document generation module 110 forreceiving the super documents 114. The document modification module 120also receives the documents 36, and based on these inputs, generates thehybrid matrix 40.

Although specific embodiments and alternatives of the present inventionhave been described and illustrated, the invention is not to be limitedto the specific forms of arrangements of parts so described andillustrated. The Claims alone, not the preceding Summary or theDescription of the Preferred Embodiment, define the invention.

1. A method for clustering documents, including generating clusters withuser perspective comprising: receiving retrieval session logs;performing log-based clustering on the session logs to generate sessionclusters; representing each of the session clusters as a log-baseddocument suitable for content based clustering; receiving a plurality ofdocuments that includes a first document that was accessed in onesession and a second document that was not accessed in any of thesessions; replacing the first document with one of the log-baseddocuments, wherein said one of the log-based documents is associatedwith the session cluster that includes the first documents; andperforming content based clustering on at least one of the log-baseddocument and the second document to generate clusters with userperspective.
 2. The method of claim 1 wherein representing each of thesession clusters as a log-based document suitable for content basedclustering includes modifying each of the log-based documents so that aEuclidean distance between the each of the log-based documents is thesame.
 3. The method of claim 1, wherein each of the session logscomprises a query used to retrieve documents.
 4. The method of claim 1,wherein each of the session logs comprises a number of documents foundto satisfy a query.
 5. The method of claim 1, wherein each of thesession logs comprises a list of documents opened by a user.
 6. Themethod of claim 1, wherein each of the session logs comprises a lengthof time that a document was opened.
 7. A method for clustering documentscomprising: generating a hybrid matrix of vectors comprising a firstvector representing a first document and a second vector representing alog-based document cluster document; and clustering the documents usingthe hybrid matrix, wherein the hybrid matrix comprises: accessingretrieval session logs; clustering retrieval sessions into sessionclusters; generating, a log-based document cluster for each sessioncluster by combining all documents opened during any retrieval sessionof the session cluster; generating a log-based document cluster vectorfor each of the log-based document clusters; replacing each document inthe log-based document cluster with the log-based document clustervector; generating an individual document vector for each document notopened during any retrieval session; and combining the log-baseddocument cluster vector and the individual document cluster vector. 8.The method of claim 7 wherein the step of clustering retrieval sessionsinto session clusters comprises the steps of: generating Boolean sessionvector for each retrieval session; forming a matrix of the Booleansession vectors; and applying a clustering algorithm to the matrix ofthe Boolean session vectors.
 9. A system for clustering documents, thesystem comprising: a storage for storing retrieval session logs; and aprocessor connected to the storage, configured to cluster the retrievalsessions into session clusters, generate, for each session cluster, alog-based document cluster, generate a log-based document cluster vectorfor each of the log-based document cluster, generate an individualdocument vector for each document not opened during any retrievalsession, cluster the documents using the log-based document clustervectors and individual document vectors.
 10. The system of claim 9wherein the documents are stored in the storage.
 11. The system of claim9 further comprising: a memory connected to the processor, for storageof a hybrid matrix comprising the log-based document cluster vectors andthe individual document vectors.
 12. A data processing system havingsession logs and documents, the system comprising: a processor forexecuting program instructions; and a media readable by the processorhaving a document clustering module having a plurality of instructions,that when executed by the processor, performs log-based clustering onthe session logs to generate session clusters, converts the sessionclusters into a form suitable for content-based clusters, performscontent-based clustering on the documents and session clusters in a formsuitable for content-based clustering to generate document clusters withusers' perspective.
 13. The system of claim 12 wherein the documentclustering module further comprises: a session vector generation modulefor receiving the session logs and based thereon for generating asession vector for each session log; a session cluster generation modulecoupled to the session vector generation module for receiving thesession vectors and based thereon for generating session clusters; ahybrid matrix builder for receiving the documents, coupled to thesession cluster generation module, for receiving the session clustersand based thereon for generating a hybrid matrix having at least onelog-based document; and a topic generation module coupled to the hybridmatrix builder for receiving the hybrid matrix and based thereon forgenerating document clusters with users' perspective.
 14. The system ofclaim 13 wherein the hybrid matrix builder further comprises: a sessiondocument generation module for receiving session clusters and basedthereon generates super documents; and document modification modulecoupled to the session document generation module for receiving thesuper documents, for receiving the documents, and based thereon forgenerating the hybrid matrix.
 15. The system of claim 12 wherein themedia is one of a floppy disk, compact disc, a volatile memory, and anon-volatile memory.